Program, information processing method, and information processing apparatus

Abstract

To provide a program or the like capable of presenting useful information for considering the purchase of a commercial real estate.SOLUTION: A program causes a computer to execute processing for obtaining real estate information about one commercial real estate, extracting, from a database in which the real estate information of a plurality of other commercial real estates is stored, the other commercial real estates having real estate information similar to that of the one commercial real estate as a similar property, and displaying the real estate information of the similar property on a display unit. Preferably, the real estate information includes an asset type indicating the use of the commercial real estate, and the other commercial real estates having the same asset type as the one commercial real estate are extracted from the database, and the other commercial real estates having similar real estate information to the one commercial real estate other than the asset type are extracted as the similar property among the extracted other commercial real estates.SELECTED DRAWING: Figure 5

Description

Technical Field

[0001] The present invention relates to a program, an information processing method, and an information processing apparatus.

Background Art

[0002] A system has been proposed that evaluates commercial real estate under consideration for purchase and presents it to a user. For example, in Patent Document 1, a real estate evaluation is calculated based on real estate information, a simulation result of the costs involved in purchasing and maintaining the real estate is calculated based on the real estate information and the real estate evaluation, and a real estate information providing system that outputs the real estate information, the real estate evaluation, and the simulation result to a user terminal device is disclosed.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In one aspect, an object is to provide a program or the like that can present useful information for considering the purchase of commercial real estate.

Means for Solving the Problems

[0005] In one aspect, the program causes a computer to execute a process of acquiring real estate information regarding one commercial real estate, extracting, as similar properties, other commercial real estates whose real estate information is similar to that of the one commercial real estate from a database in which real estate information of a plurality of other commercial real estates is stored, and displaying the real estate information of the similar properties on a display unit.

Effects of the Invention

[0006] On one side, it is possible to present information useful for considering the purchase of commercial real estate.

Brief Description of the Drawings

[0007]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Modes for Carrying Out the Invention

[0008] Hereinafter, the present invention will be described in detail based on the drawings showing its embodiments. (Embodiment 1) FIG. 1 is an explanatory diagram showing a configuration example of a real estate management system. In the present embodiment, based on information on other commercial real estate similar to the commercial real estate for sale (income property assumed to obtain income by leasing), a real estate management system that presents (displays) information for considering the purchase of the commercial real estate (considering whether to purchase it) will be described. The real estate management system includes an information processing device 1 and a terminal 2. Each device is communicatively connected via a network N such as the Internet.

[0009] In the following description, for the sake of brevity, the commercial real estate under consideration for purchase is referred to as the "target property", and other commercial real estate similar to the target property is referred to as the "similar property". Also, when referring to any commercial real estate without distinguishing between the target property and the similar property, the commercial real estate is simply referred to as the "property".

[0010] The information processing device 1 is an information processing device capable of various information processing and information transmission and reception, such as a server computer, a personal computer, etc. In this embodiment, it is assumed that the information processing device 1 is a server computer, and hereinafter, for the sake of brevity, it is read as server 1. Server 1 receives an input from a user to register the target property for sale, and provides a service that allows browsing of the information of the registered target property. In this embodiment, server 1 presents, as one of the information of the target property, information on similar properties similar to the target property (such as the acquisition price of the similar property, the appraisal value, etc.) (see FIG. 5).

[0011] The terminal 2 is a terminal device used by the user of this system, such as a personal computer, a smartphone, a tablet terminal, etc. As will be described later, when the terminal 2 displays a list of target properties registered by the user and receives an input to select any target property from the list, it displays information on similar properties similar to the selected target property.

[0012] Note that the user of this system is not limited to a person who purchases a property, and may be an intermediary who introduces a property to a person who wishes to purchase a property.

[0013] FIG. 2 is a block diagram showing a configuration example of server 1. Server 1 includes a control unit 11, a main memory unit 12, a communication unit 13, and an auxiliary storage unit 14. The control unit 11 has one or more processors such as a CPU (Central Processing Unit), MPU (Micro-Processing Unit), and GPU (Graphics Processing Unit), and performs various information processes by reading and executing the program P stored in the auxiliary storage unit 14. The main storage unit 12 is a temporary storage area such as SRAM (Static Random Access Memory) and DRAM (Dynamic Random Access Memory), and temporarily stores data necessary for the control unit 11 to execute arithmetic processing. The communication unit 13 is a communication module for performing communication-related processes, and transmits and receives information to and from the outside.

[0014] The auxiliary storage unit 14 is a non-volatile storage area such as a large-capacity memory and a hard disk, and stores the program P (program product) and other data necessary for the control unit 11 to execute processing. Further, the auxiliary storage unit 14 stores an importance table 141, a real estate DB 142, a tenant information DB 143, a recruitment information DB 144, and a route price DB 145. The importance table 141 is a table referred to when extracting similar properties, and is a table that defines the importance of each feature amount (item) of the real estate information of the property. The real estate DB 142 is a database that stores the real estate information of each property. The tenant information DB 143 is a database that stores tenant information regarding the tenants living in each property. The recruitment information DB 144 is a database that stores the recruitment information (vacancy information of the property) of the tenants in each property. The route price DB 145 is a database that stores the route price in each area on the map.

[0015] Note that the auxiliary storage unit 14 may be an external storage device connected to the server 1. Further, the server 1 may be a multi-computer composed of a plurality of computers, or may be a virtual machine virtually constructed by software.

[0016] In addition, in this embodiment, the server 1 is not limited to the above configuration, and may include, for example, an input unit that receives operation inputs, a display unit that displays images, and the like. Further, the server 1 may be provided with a reading unit that reads a portable storage medium 1a such as a CD (Compact Disk)-ROM or a DVD (Digital Versatile Disc)-ROM, and reads and executes the program P from the portable storage medium 1a.

[0017] FIG. 3 is an explanatory diagram showing an example of the record layouts of the importance table 141, the real estate DB 142, the tenant information DB 143, the recruitment information DB 144, and the route price DB 145. The importance table 141 includes a feature amount column and an importance column. The feature amount column stores each feature amount name included in the real estate information of the property. The importance column stores the importance of each feature amount in association with the feature amount.

[0018] The real estate DB 142 includes a property ID column, a property name column, and a real estate information column. The property ID column stores a property ID for identifying each property. The property name column and the real estate information column store the property name (building name) and other real estate information in association with the property ID, respectively. The real estate information column stores, for example, the address of the property, the asset type (usage such as residential or office), the nearest station, and the like.

[0019] The tenant information DB 143 includes a tenant ID column, a tenant name column, an occupied property column, and a tenant information column. The tenant ID column stores a tenant ID for identifying each tenant. The tenant name column, the occupied property column, and the tenant information column store the tenant name, the occupied property name, and other tenant information in association with the tenant ID, respectively. The tenant information column stores, for example, the business type of the tenant (company, etc.) and the number of people (number of employees).

[0020] The recruitment information DB144 includes a recruitment ID column and a recruitment information column. The recruitment ID column stores the recruitment ID for identifying each recruitment case of the tenant. The recruitment information column stores the tenant's recruitment information in association with the recruitment ID. The recruitment information column stores, for example, the name of the property for which the tenant is being recruited, the leased portion, the area of the leased portion, etc.

[0021] The route value DB145 includes an area column, a route number column, a location information column, and a price information column. The area column stores each area on the map. The route number column, the location information column, and the price information column each store, in association with the area, the number of each route (the road facing the property) within the area, the location information on the map of the route, and the information on the route value corresponding to the route.

[0022] Figures 4 to 6 are explanatory diagrams showing examples of the display screens of the terminal 2. Based on Figures 4 to 6, the outline of the present embodiment will be described.

[0023] Figure 4 is an example of a display screen at the time of registering a target property. When the terminal 2 receives an operation input to the case registration button 41 displayed in the upper right of the screen, the screen shown in Figure 4 is displayed. The screen includes a registration column 42. The registration column 42 is an input column for newly registering the target property. The terminal 2 receives inputs such as the name of the target property, the asset type, and the information acquisition date as essential input items via the registration column 42. Also, the terminal 2 receives the upload of the property summary document (introduction materials of the target property) of the target property via the registration column 42.

[0024] The server 1 acquires the real estate information of the target property to be registered. For example, the terminal 2 receives inputs from the user about details of the target property, such as the introducing company of the target property (the intermediary introducing the property, the company or individual owning the property, etc.), the name of the person in charge of the introducing company, and the name of the property owner, via the registration column 42. Alternatively, the server 1 receives the input of the real estate information described in the uploaded property summary document from the operator (administrator) of the present system.

[0025] In the present embodiment, it is described that the user or operator manually inputs real estate information. However, the server 1 may automatically extract real estate information from the property summary document.

[0026] The real estate information may be any information related to the property, and its content is not particularly limited. The real estate information includes, for example, property name, asset type, address, nearest station, completion year, site area, floor area, rentable area, floor area ratio, building coverage ratio, etc. In particular, in the present embodiment, the real estate information includes tenant information of the property, vacancy information (tenant recruitment information), sale price, rentable area, and return information.

[0027] The tenant information is information about the tenant living in the property and includes the tenant's industry information and employee information. By including the tenant information in the real estate information, when extracting similar properties described later, based on the hypothesis that the transfer and transfer tendencies and demographic attributes (visitor attributes) are similar according to the tenant's industry, etc., properties with similar revenue and cost structures can be extracted.

[0028] The vacancy information is information indicating the vacancy status of the property and includes the vacancy rate of the property, etc. By including the vacancy rate information in the real estate information, when extracting similar properties described later, properties with similar revenue structures can be extracted. Also, by taking the high or low of the vacancy rate of each property compared with the vacancy rate of the area as a feature quantity, properties with similar relative positions within the area can be extracted.

[0029] In addition, by including the sale price, rentable area, and return information (NOI return, surface return, etc.) of the property in the real estate information, when extracting similar properties described later, fine differences can be captured for properties with the same level of each feature quantity.

[0030] Since tenant information and vacancy information are unknown to the user, Server 1 obtains this information from various databases (Tenant Information DB 143, Recruitment Information DB 144) prepared in advance. Specifically, when Server 1 receives registration of real estate information from the user or operator, based on specific information (such as the property name, address, etc. of the target property) included in the real estate information, Server 1 obtains tenant information and vacancy information (recruitment information) corresponding to the target property from Tenant Information DB 143 and Recruitment Information DB 144. Server 1 combines the obtained tenant information and vacancy information with the registered real estate information (for example, associates and stores the real estate information with the obtained tenant information and vacancy information).

[0031] In addition, Server 1 may also obtain the route price corresponding to the property, pedestrian flow data around the property, etc., and combine them with the real estate information.

[0032] Server 1 stores the data (real estate information) of the target property registered as described above in Real Estate DB 142 as data of a new property.

[0033] Figure 5 is an example of a management screen for the target property. The management screen includes a list 51. List 51 is a list that shows the target properties registered by the user in tabular form. As shown in Figure 5, information such as the property name, asset type, nearest station, etc. of the target property is displayed in list 51.

[0034] Terminal 2 receives an input to select any one of the target properties from list 51. When a target property is selected, terminal 2 displays a property display column 52. Property display column 52 is a display column that displays information related to the selected target property. Terminal 2 displays the real estate information (not shown in Figure 5) of the target property in property display column 52.

[0035] In this embodiment, the terminal 2 further displays, in the property display column 52, information on similar properties having real estate information with feature quantities similar to those of the target property, as information for considering the purchase of the target property. Specifically, as shown in FIG. 5, the terminal 2 displays, in addition to the property name and address of each similar property similar to the target property, the latest acquisition price (and acquisition date), appraisal value (and appraisal date), loanable per-square-meter price, and return rate (Cap Rate) of the similar property.

[0036] Hereinafter, the processing content for extracting similar properties will be described. The server 1 calculates the similarity between the target property selected by the user above and each property whose real estate information is stored (registered) in the property DB 142, and extracts similar properties based on the calculated similarity.

[0037] Specifically, the server 1 first calculates the feature quantities necessary for calculating the similarity from the real estate information. For example, the server 1 calculates the vacancy rate of the property by dividing the rentable area of the rental part that is recruiting tenants by the loanable area. Similarly, the server 1 calculates the ratio of the potential vacancy rate (the vacancy rate considering the rental parts where tenants are scheduled to move out) to the vacancy rate, the aggregated value in a predetermined area unit, the ratio of the property vacancy rate to the area vacancy rate (the value obtained by dividing the rentable area of the rental part that is recruiting tenants in the entire area by the loanable area of the entire area). For each feature quantity calculated in this way, the server 1 performs processing such as dummyization and standardization for similarity calculation.

[0038] Next, the server 1 refers to the importance table 141 that defines the importance of each feature quantity of the real estate information, and weights each feature quantity. As illustrated in FIG. 3, in the importance table 141, the importance of each feature quantity is defined step by step in the form of "High", "Middle", and "Low". The server 1 weights each feature quantity according to the importance, and vectorizes the real estate information of each property. By defining the importance of each feature quantity and weighting each feature quantity according to the importance, similar properties can be extracted more suitably.

[0039] Server 1 calculates the similarity between the target property and each property registered in the real estate DB142 based on the vectorized real estate information. For example, Server 1 calculates the Euclidean distance as the similarity. Server 1 compares the calculated similarity with a predetermined threshold value and extracts the properties with a similarity equal to or higher than the threshold value as similar properties.

[0040] Note that Server 1 may also extract the properties with the top certain number of similarities as similar properties.

[0041] Also, in this embodiment, it is described that similar properties are extracted based on rules, but this embodiment is not limited to this, and similar properties may be extracted using a machine learning model.

[0042] Server 1 causes the information of the extracted similar properties to be displayed in the property display column 52. Specifically, as described above, Server 1 displays the latest acquisition price, appraisal value, loanable per-square-meter price, and return on investment of the similar properties. For example, Server 1 displays this information in order from the property with the highest similarity.

[0043] FIG. 6 is an example of a display screen of detailed information of similar properties. When the terminal 2 receives an input to select any one of the similar properties in the property display column 52 of FIG. 5, the terminal 2 transitions to the screen shown in FIG. 6 and displays the detailed information of the selected similar property.

[0044] Specifically, as shown on the left side of FIG. 6, the terminal 2 displays the real estate information (address, nearest station, asset type, information acquisition date, etc.) of the similar property. Further, the terminal 2 displays a graph showing the past transitions of the acquisition price, appraisal value, loanable per-square-meter price, and return on investment, whose latest values are displayed in the property display column 52, on the right side of the screen (the loanable per-square-meter price and return on investment are not shown in FIG. 6). Thereby, the user can confirm how the acquisition price and the like have changed.

[0045] Note that in this embodiment, although the past transition of the acquisition price of similar properties is displayed in a graph format, it may also be displayed in a table format or the like. That is, the terminal 2 only needs to be able to display the past transition information of the acquisition price of similar properties and the like.

[0046] As described above, according to this embodiment, similar properties having real estate information with characteristics similar to those of the target property being sold are extracted, and information for considering the purchase of the target property is displayed based on the real estate information of the extracted similar properties. Thereby, it is possible to consider whether or not to purchase the target property while comparing similar properties.

[0047] FIG. 7 is a flowchart showing an example of the processing procedure executed by the server 1. Based on FIG. 7, the processing content executed by the server 1 will be described. The control unit 11 of the server 1 receives an input from the user to register the target property being sold via the terminal 2 (step S11). For example, the control unit 11 receives an input of the property name of the target property and the like via the registration column 42 illustrated in FIG. 4.

[0048] The control unit 11 acquires the real estate information of the target property (step S12). For example, the control unit 11 may receive an input of real estate information from the user via the registration column 42, or may receive an upload of a property summary document from the user and receive an input of the real estate information described in the uploaded summary document from the operator.

[0049] Based on the property name, address, etc. of the target property included in the acquired real estate information, the control unit 11 acquires tenant information regarding the tenant living in the target property and vacancy information (tenant recruitment information) of the target property from the tenant information DB 143 and the recruitment information DB 144 (step S13). The control unit 11 combines the acquired tenant information and vacancy information with the real estate information of the target property (step S14).

[0050] The control unit 11 outputs and displays on the terminal 2 a list 51 of target properties registered by the user (step S15). The control unit 11 receives an input for selecting any one of the target properties from the list 51 (step S16).

[0051] The control unit 11 extracts from the real estate DB 142 similar properties having real estate information with feature amounts similar to those of the selected target property (step S17). Specifically, the control unit 11 refers to a table that defines the importance of each feature amount of the real estate information, weights each feature amount of the real estate information of each property, and vectorizes it. The control unit 11 calculates the similarity between the target property and each property registered in the real estate DB 142 based on the vectorized real estate information. The control unit 11 extracts as similar properties those properties whose calculated similarity is equal to or greater than a threshold value.

[0052] The control unit 11 outputs and displays on the terminal 2 information related to the target property selected in step S16 (step S18). Specifically, the control unit 11 displays the information of the target property in the property display column 52 and also displays the information of each similar property extracted in step S17 in the property display column 52. For example, as the information of the similar property, the control unit 11 displays, in addition to the property name and address of the similar property, the latest acquisition price (and acquisition date), the appraisal value (and appraisal date), the loanable per-square-meter unit price, and the return on investment (Cap Rate).

[0053] The control unit 11 receives an input for selecting any one from the displayed similar properties (step S19). The control unit 11 outputs and displays on the terminal 2 the detailed information of the selected similar property (step S20). Specifically, as shown in FIG. 6, the control unit 11 displays, in addition to the real estate information of the similar property, a graph 61 showing the past trends of the acquisition price, the appraisal value, the loanable per-square-meter unit price, and the return on investment of the similar property. The control unit 11 ends a series of processes.

[0054] As described above, according to the first embodiment, it is possible to present useful information for considering the purchase of commercial real estate.

[0055] (Second Embodiment) In this embodiment, based on the real estate information of similar properties, a future predicted value of a variable value related to the target property (for example, the selling price, yield, etc. of the target property) is calculated and presented to the user. Note that the same reference numerals are used for the content overlapping with Embodiment 1, and the description thereof is omitted.

[0056] FIG. 8 is an explanatory diagram showing a display example of the property display column 52 according to Embodiment 2. Based on FIG. 8, the outline of this embodiment will be described.

[0057] In this embodiment, the server 1 calculates a future predicted value of a variable value related to the target property based on the real estate information of similar properties, and causes it to be displayed in the property display column 52. The variable value is, for example, the selling price, yield, etc. of the property. In this embodiment, the variable value is described as being the selling price of the property.

[0058] Note that in the above, as examples of the variable value, numerical values related to the income obtained from the sale or lease of the property, such as the selling price and yield, are given, but this embodiment is not limited thereto. For example, the variable value may be the occupancy rate of the property (leased area / available lease area). Thus, the variable value may be any numerical value that can vary depending on the time and circumstances.

[0059] For example, the server 1 calculates the predicted value of the selling price of the target property at each time point (for example, every year) in the next 10 years. Specifically, the server 1 calculates the predicted value of the selling price of the target property based on the actual values of the past selling prices of the similar properties and the actual values of the past selling prices of the target property. For example, the server 1 calculates the predicted value at each time point every year in the future using regression analysis based on these numerical values.

[0060] In addition, in this embodiment, regression analysis is used as the algorithm for calculating the predicted value. However, this embodiment is not limited to this, and a machine learning model may be used to calculate the predicted value. That is, when the actual values of the transaction prices (fluctuation values) of similar properties (and the target property) are input, the server 1 generates (learns) a machine learning model that outputs the predicted value of the transaction price of the target property from the actual values of past similar properties and transaction prices, and inputs the actual values of the transaction prices of similar properties into the model to calculate the predicted value of the transaction price of the target property.

[0061] In addition, it is preferable that the server 1 refers to the land price corresponding to the target property in addition to the actual values of the transaction prices of similar properties and the target property, and calculates the predicted value of the transaction price of the target property. For example, the server 1 uses the route price corresponding to the target property as the land price. The server 1 acquires the route price corresponding to the target property from the route price DB 145, and calculates the predicted value of the transaction price of the target property based on the acquired route price and the actual values of the transaction prices of similar properties and the target property. Thereby, the predicted value of the transaction price can be predicted more preferably.

[0062] In addition, depending on the real estate information of similar properties, it is preferable for the server 1 to exclude the real estate information of the similar properties from the calculation criteria of the predicted value. For example, when the vacancy rate of a similar property is equal to or higher than a predetermined value, the server 1 excludes the actual value of the transaction price of the similar property from the calculation criteria. Thereby, similar properties that are unsuitable for calculating the transaction price can be excluded from the calculation criteria.

[0063] The server 1 outputs the calculated predicted value to the terminal 2 and displays it in the property display column 52. For example, as shown in FIG. 8, the terminal 2 displays the predicted value of the transaction price after 10 years and also displays a graph showing the transition of the actual value and the predicted value of the transaction price. In the graph, the solid line represents the actual value and the dotted line represents the predicted value.

[0064] In addition, when the terminal 2 displays a graph, as shown in FIG. 8, it is preferable to display the selling prices of similar properties side by side in addition to the selling price of the target property. In FIG. 8, the selling price of the target property is illustrated by a thick line, and the selling prices of similar properties are illustrated by thin lines. The terminal 2 displays, in a graph, the transition of the actual and predicted values of the selling prices of similar properties together with the actual and predicted values of the selling price of the target property. Note that the server 1 may calculate predicted values of the selling price for similar properties based on real estate information of other properties similar to the similar properties, in the same manner as for the target property. As a result, the user can suitably predict how the selling price of the target property will fluctuate.

[0065] FIG. 9 is a flowchart showing an example of a processing procedure executed by the server 1 according to Embodiment 2. After extracting similar properties (step S17), the server 1 executes the following processing. The control unit 11 of the server 1 calculates a future predicted value of a variation value related to the target property based on the real estate information of the extracted similar properties (step S201). The variation value is, for example, the selling price or yield of a property. The control unit 11 calculates a predicted value of the variation value at a future time based on the actual value of the variation value of the similar property, the actual value of the variation value of the target property, and the land price (for example, route price) corresponding to the target property, in addition to the actual value of the variation value of the similar property.

[0066] The control unit 11 outputs the predicted value calculated in step S201 to the terminal 2 for display, in addition to the information of the target property and the information of the similar properties (step S202). Specifically, the control unit 11 causes the predicted value at a future time (for example, 10 years later) to be displayed, and also causes a graph showing the transition of the actual and predicted values of the variation value of the target property to be displayed. When displaying the graph, the control unit 11 causes the transition of the actual and predicted values of the variation value of the similar property to be displayed side by side in addition to the transition of the actual and predicted values of the target property. The control unit 11 proceeds to step S19.

[0067] As described above, according to the second embodiment, by using the real estate information of similar properties, it is possible to suitably predict a future predicted value of a variation value (such as a selling price) related to the target property and present it to the user.

[0068] In the second embodiment, when calculating a predicted value using a machine learning model, regarding the future predicted value of the calculated variable value (such as the buying and selling price) related to the target property, after the calculation, if the server 1 acquires information on the actual value of the variable value of the target property (such as the buying and selling price when the target property is actually bought and sold), the machine learning model may be updated using the actual value as learning data.

[0069] (Modification Example 1) In this modification example, a form in which similar properties similar to the target property are extracted from the real estate DB142 by narrowing down to properties with the same asset type as the target property will be described.

[0070] As described in the first embodiment, the server 1 receives an input of real estate information regarding the target property and extracts similar properties from the real estate DB142 whose feature amounts of the target property and the real estate information are similar. The real estate information includes, in addition to tenant information, vacancy information, etc., an asset type indicating the type of use of the property.

[0071] In this modification example, when the server 1 extracts similar properties, it first extracts properties with the same asset type as the target property. By performing narrowing down according to the asset type, it is possible to prevent a situation where, for example, a residential property is extracted as a similar property although an office property is desired to be investigated.

[0072] In addition, when the user inputs real estate information including the asset type for the target property, there may be cases where "Other" is specified as the asset type and the specific content of the asset type is not registered, or the asset type is not input at all, making it impossible to clearly identify the asset type. In such cases, the server 1 may estimate the asset type of the target property based on real estate information other than the asset type. For example, the server 1 may estimate the asset type from the floor area ratio, fire resistance standards, etc. of the target property. Alternatively, the server 1 may estimate the asset type from information such as the address of the building already built on the land related to the target property and the regulations applicable to the address. Alternatively, the server 1 may estimate the asset type using a machine learning model instead of a rule-based approach.

[0073] Also, when the asset type cannot be clearly identified for other properties (candidates for similar properties) registered in the real estate DB 142, the asset type of the other commercial real estate may be estimated from real estate information other than the asset type. That is, when extracting properties of the same asset type as the target property, for properties registered with the asset type as "Other" etc., the asset type may be estimated from real estate information other than the asset type, and the properties may be extracted according to the estimated asset type.

[0074] Note that when estimating the asset type, the server 1 may estimate multiple asset types (for example, estimated as available both as "for office" and as "for residential") as candidates for the asset type of the target property.

[0075] The server 1 extracts, as similar properties, properties among those with the same asset type as the target property that are similar in real estate information to the target property. That is, the server 1 vectorizes the real estate information of the target property and the real estate information of each property extracted as having the same asset type, calculates the similarity, and extracts similar properties.

[0076] In this case, the server 1 may change the extraction method when extracting similar properties according to the asset type of the target property. Specifically, the server 1 changes the feature amount (item) of the real estate information that is weighted as having a high importance according to the asset type. For example, when the target property is a residential property, the equipment information of the target property is weighted as having a high importance. Also, when the target property is an office property, the return information (Cap Rate) of the target property is weighted as having a high importance. Alternatively, when adopting a machine learning model as the extraction algorithm for similar properties, the server 1 may extract similar properties using different models according to the asset type. In this way, by changing the extraction method of similar properties according to the asset type, appropriate similar properties can be extracted for each asset type.

[0077] (Modification Example 2) In this modification example, a form of extracting similar properties by referring to the browsing history of properties by the user when extracting similar properties will be described.

[0078] As described in Embodiment 1, the server 1 causes the terminal 2 to display information such as the acquisition price of one or more similar properties similar to the target property as information for considering the purchase of the target property. When receiving an input to select any one of the one or more similar properties, the server 1 causes the terminal 2 to display the detailed information of the selected similar property. In this modification example, the server 1 stores the display history of the similar property as the browsing history of properties by the user.

[0079] When the server 1 newly extracts similar properties and presents them to the user, it extracts as similar properties those properties whose real estate information is similar to that of the target property and whose real estate information is similar to that of the properties browsed by the user in the past.

[0080] For example, server 1 predicts the asset type of the properties that the user is likely to view according to the asset type of each property the user has viewed in the past, narrows down to that asset type, and extracts properties similar to the target property. Alternatively, server 1 may change the feature quantities to be weighted when extracting similar properties based on the real estate information of each property the user has viewed in the past, etc.

[0081] In this way, by referring to the browsing history of properties by the user, the tendency of the properties viewed by the user may be analyzed, and properties that match the tendency may be extracted.

[0082] (Modification Example 3) In this modification example, a form of extracting similar properties based on the public information that is publicly available to all users and the non-public information that is publicly available to the users who input (upload) the real estate information among the real estate information of each property will be described.

[0083] As described in Embodiment 1, server 1 receives the input of the real estate information of the target property from the user. The input real estate information is stored (registered) in the real estate DB 142 as a candidate for similar properties.

[0084] Here, in this modification example, server 1 classifies the real estate information of each property into public information that is publicly available to all users and non-public information that is non-public to users other than the user who input the real estate information.

[0085] The public information is, for example, the asset type of the property, the address, etc., and is information that is generally publicly available. On the other hand, the non-public information is, for example, the rent of the property, etc., and is information that the user has collected by himself / herself and registered in the real estate DB 142. In the real estate DB 142, the non-public information is stored in association with the user who input the non-public information.

[0086] When calculating the similarity between the real estate information of the target property and the real estate information of each other property, Server 1 calculates the similarity based on the public information of each property and the non-public information that can be made public to the users who are the viewing targets of the similar properties (associated with the viewing target users). That is, when the user who is the viewing target is the input source of the real estate information of that property, Server 1 calculates the similarity with the target property by referring to not only the public information but also the non-public information. On the other hand, when the user who is the viewing target is not the input source of the real estate information, Server 1 calculates the similarity with the target property from the public information without referring to the non-public information. Server 1 extracts the properties with a similarity equal to or higher than the threshold as similar properties and displays the information of the similar properties on Terminal 2.

[0087] In this way, the real estate information of each property can be classified into public information and non-public information, and the non-public information can be used appropriately according to the users who view the similar properties.

[0088] It should be considered that all aspects of the embodiments disclosed this time are illustrative and not restrictive. The scope of the present invention is indicated by the claims, rather than the above-mentioned meaning, and it is intended that all modifications within the meaning and scope equivalent to the claims are included.

[0089] The matters described in each embodiment can be combined with each other. Also, the independent claims and dependent claims described in the claims can be combined with each other in all possible combinations regardless of the citation form. Furthermore, although the claims use a form (multi-claim form) of describing claims that cite two or more other claims, it is not limited to this. A form of describing a multi-claim (multi-multi-claim) that cites at least one multi-claim may also be used.

[0090] At least a part of the processes described as being executed by a specific device in this specification may be executed by any information processing device. For example, at least a part of the processes described as being executed by the server in the above embodiment may be executed by each terminal. Conversely, at least a part of the processes described as being executed by each terminal in the above embodiment may be executed by the server, and each terminal may function only as an interface with the user such as input / output.

[0091] Note that the series of processes by each device described in this specification may be realized using any of software, hardware, and a combination of software and hardware. The program constituting the software is, for example, pre-stored in a recording medium (specifically, a non-transitory storage medium readable by a computer) provided inside or outside each device. Then, each program is read into the RAM when executed by a computer that controls each device described in this specification, and is executed by a processing circuit such as a CPU. The above recording medium is, for example, a magnetic disk, an optical disk, a magneto-optical disk, a flash memory, etc. Also, the above computer program may be distributed via a network, for example, without using a recording medium. Also, the above computer may be a specific-purpose integrated circuit such as an ASIC, a general-purpose processor that executes functions by reading a software program, or a computer on a server used for cloud computing, etc. Also, the series of processes by each device described in this specification may be processed centrally by a single computer, or may be processed distributively by a plurality of computers. Further, in each of the above embodiments, two or more communication means existing in one device may be physically realized by one medium.

[0092] Also, the processes described using a flowchart or a sequence diagram in this specification do not necessarily have to be executed in the order shown in the figures. Some processing steps may be executed in parallel. Also, additional processing steps may be adopted, and some processing steps may be omitted.

[0093] The present invention is not limited to the above-described embodiments, and various modifications are possible, and it goes without saying that they are also included within the scope of the present invention.

Explanation of Reference Numerals

[0094] 1 Server (information processing apparatus) 11 Control unit 12 Main memory unit 13 Communication unit 14 Auxiliary storage unit P Program 141 Importance table 142 Real estate DB 143 Tenant information DB 144 Recruitment information DB 145 Route price DB 2 Terminal

Claims

1. Obtain real estate information for a single commercial property, Extracting, from a database in which real estate information of a plurality of other commercial properties is stored, the other commercial properties having real estate information similar to that of the one commercial property as similar properties; The real estate information of the similar property is displayed on the display unit. A program that causes a computer to carry out processing.

2. The real estate information includes an asset type indicating a use of the commercial real estate; Extracting from the database the other commercial properties that have the same asset type as the one commercial property; Among the extracted other commercial real estate properties, the other commercial real estate properties having similar real estate information other than the asset type to the one commercial real estate property are extracted as the similar properties. The program according to claim 1.

3. if the asset type cannot be clearly identified in the real estate information of the one commercial property, estimating the asset type of the one commercial property based on real estate information other than the asset type; Extracting from the database other commercial properties having the same asset type as the estimated asset type. The program according to claim 2.

4. Predicting a plurality of asset type candidates for the one commercial property based on property information other than the asset type. The program according to claim 3.

5. Changing an extraction method for extracting the similar properties according to the asset type of the one commercial real estate. The program according to claim 2.

6. Acquire real estate information including the asset type by accepting input of real estate information of the one commercial real estate with the asset type as a required input item. The program according to claim 2.

7. storing a display history of the similar properties whose real estate information was displayed to the user in a storage unit as a browsing history of commercial real estate by the user; When extracting the similar properties, the other commercial properties having real estate information similar to that of the one commercial property and similar to that of the commercial property viewed by the user are extracted as the similar properties. The program according to claim 1.

8. receiving input of real estate information for each commercial property from a user, acquiring the real estate information for the one commercial property, and storing the real estate information for the other commercial property in the database; The real estate information includes public information that is public to all users and private information that is private to users other than the user who inputs the real estate information, When extracting the similar properties, the similar properties are extracted based on the public information of each commercial real estate and the private information that can be made public to users who are the viewing targets of the similar properties. The program according to claim 1.

9. vectorizing real estate information of the one commercial property and real estate information of each of the plurality of other commercial properties; Calculating a similarity between the one commercial property and the plurality of other commercial properties based on the vectorized real estate information; Based on the calculated similarity, the other commercial real estate that is similar to the one commercial real estate is extracted as the similar property. The program according to claim 1.

10. As real estate information of the similar property, information on the acquisition price, appraisal value, loanable price per tsubo, or yield of the similar property is displayed on the display unit. The program according to claim 1.

11. The real estate information includes a commercial real estate property name or address; When real estate information of the one commercial property is obtained, tenant information or vacancy information of the one commercial property is obtained based on the property name or address of the one commercial property; Adding the acquired tenant information or vacancy information to real estate information for the one commercial property; Based on the real estate information to which the tenant information or vacancy information has been added, the other commercial real estate similar to the one commercial real estate is extracted as the similar property. The program according to claim 1.

12. calculating a future forecast of the variance of the one commercial property based on actual variances associated with the similar properties and the actual variance of the one commercial property; The calculated predicted value is displayed on a display unit. The program according to claim 1.

13. The variable value is the purchase price, yield, or occupancy rate of commercial real estate. The program according to claim 12.

14. Obtain real estate information for a single commercial property, Extracting, from a database in which real estate information of a plurality of other commercial properties is stored, the other commercial properties having real estate information similar to that of the one commercial property as similar properties; The real estate information of the similar property is displayed on the display unit. An information processing method in which processing is performed by a computer.

15. An information processing device including a control unit, The control unit: Obtain real estate information for a single commercial property, Extracting, from a database in which real estate information of a plurality of other commercial properties is stored, the other commercial properties having real estate information similar to that of the one commercial property as similar properties; The real estate information of the similar property is displayed on the display unit. Information processing device.

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